Systems and methods for using geometry sensitivity information for guiding workflow

ABSTRACT

Systems and methods are disclosed for using geometry sensitivity information for guiding workflows in order to produce reliable models and quantities of interest. One method includes determining a geometric model associated with a target object; determining one or more quantities of interest; determining sensitivity information associated with one or more subdivisions of the geometric model and the one or more quantities of interest; and generating, using a processor, a workflow based on the sensitivity information.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally tomedical imaging and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods forusing sensitivity information for guiding workflow.

BACKGROUND

Workflows are tools used to guide any process from start to finish in anorganized, predictable fashion. Ideal workflows enhance efficiency whiledrawing attention to possible areas that may require scrutiny. Ingeneral, a workflow is comprised of a series of connected steps,typically automated or semi-automated and processed in sequence. Outputsand/or a subset of outputs from previous steps may be used as inputs insubsequent steps such that each step builds on previous steps. A guidedworkflow may be comprised of a semi-automated process where manualcorrections may be made to the workflow and a sub-sequence of theworkflow may be reprocessed. Intrusion (e.g., guiding a workflow) may betriggered, for example, by algorithmic error, inability to capturesalient features, failure to output results, etc.

Often, workflows are built around calculating a quantity of interest orpreparing preliminary information to provide a foundation forcalculating quantities of interest. Such preliminary information mayinclude, for example, a geometric model. In some instances, quantitiesof interest are especially affected by geometry. For example, quantitiesof interest including air flow patterns and drag across the wing of anaircraft or exterior shell of an automobile are dependent on modelgeometry. However, geometries of models may have some uncertainty due,for example, to problems with images from which the models are made. Forexample, where the images are scans from medical imaging, problems withthe images may include motion and registration artifacts, bloomingartifacts, etc. Such uncertainty may impact computation of quantities ofinterest. Geometry sensitivity, then, may be defined as how muchuncertainty in geometry may impact the computation of quantities ofinterest. In other words, sensitivity may describe the extent or amountto which geometry uncertainty affects a quantity of interestcalculation.

Thus, a need exists for focusing attention on regions of a model thatexhibit higher sensitivity, meaning greater impact on a quantity ofinterest contributed by uncertainty in geometry. These regions may bespecific regions of an image where computations for quantities ofinterest may be sensitive to reconstructed geometry. A need exists foridentifying regions of geometric models based on sensitivity andcreating workflows that permit attention to and/or correction of theseregions. More specifically, a need exists for guided workflows that maydraw attention to highly sensitive regions in a model, for example, inthe context of workflows guided by geometry sensitivity.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for guiding a workflow based on geometrysensitivity information. One method includes: determining a geometricmodel associated with a target object; determining one or morequantities of interest; determining sensitivity information associatedwith one or more subdivisions of the geometric model and the one or morequantities of interest; and generating, using a processor, a workflowbased on the sensitivity information.

In accordance with another embodiment, a system for guiding a workflow,comprises: a data storage device storing instructions for guiding aworkflow using geometry sensitivity information; and a processorconfigured for: determining a geometric model associated with a targetobject; determining one or more quantities of interest; determiningsensitivity information associated with one or more subdivisions of thegeometric model and the one or more quantities of interest; andgenerating, using a processor, a workflow based on the sensitivityinformation.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofguiding a workflow based on geometry sensitivity information isprovided. The method includes: determining a geometric model associatedwith a target object; determining one or more quantities of interest;determining sensitivity information associated with one or moresubdivisions of the geometric model and the one or more quantities ofinterest; and generating, using a processor, a workflow based on thesensitivity information.

Another method includes: obtaining a geometric model associated with atarget object; determining one or more parameters associated with thegeometric model; determining sensitivity information associated with asensitivity of one or more quantities of interest in relation to the oneor more parameters; and altering, using a processor, a workflow forinteracting with the geometric model based on the sensitivityinformation.

In accordance with another embodiment, a system for guiding a workflow,comprises: a data storage device storing instructions for guiding aworkflow using geometry sensitivity information; and a processorconfigured for: obtaining a geometric model associated with a targetobject; determining one or more parameters associated with the geometricmodel; determining sensitivity information associated with a sensitivityof one or more quantities of interest in relation to the one or moreparameters; and altering, using a processor, a workflow for interactingwith the geometric model based on the sensitivity information.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofguiding a workflow based on geometry sensitivity information isprovided. The method includes: obtaining a geometric model associatedwith a target object; determining one or more parameters associated withthe geometric model; determining sensitivity information associated witha sensitivity of one or more quantities of interest in relation to theone or more parameters; and altering, using a processor, a workflow forinteracting with the geometric model based on the sensitivityinformation.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network for usinggeometry sensitivity information to guide workflow, according to anexemplary embodiment of the present disclosure.

FIG. 2 is a block diagram of an exemplary method for using geometrysensitivity information for guiding workflow, according to an exemplaryembodiment of the present disclosure.

FIG. 3A is a block diagram of an exemplary method for determining thesensitivity of a quantity of interest for each subdivision, according toan exemplary embodiment of the present disclosure.

FIG. 3B is a block diagram of an exemplary method for calculatingquantities of interest, according to an exemplary embodiment of thepresent disclosure.

FIG. 4 is a block diagram of an exemplary method for guiding users toinspect subdivisions of a model, image, and/or geometry based onsensitivity, according to an exemplary embodiment of the presentdisclosure.

FIGS. 5A-5C are block diagrams of a specific embodiment of usinggeometry sensitivity information for guiding workflow for a coronarymodel, according to an exemplary embodiment.

FIG. 6 is a block diagram of an exemplary method of a workflow processguided using sensitivity information, according to an exemplaryembodiment.

FIG. 7 is a diagram of an exemplary user interface that a user may viewas part of a guided workflow, according to an exemplary embodiment.

FIG. 8 is a simplified block diagram of an exemplary computer system inwhich embodiments of the present disclosure may be implemented.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

As described above, workflows may be involved in any automated orsemi-automated process. Outputs from previous steps may be used asinputs in subsequent steps. Guided workflows may include asemi-automated process where manual corrections may be made to aworkflow. In one embodiment, outputs of the workflow may be reprocessedand/or calculated based on corrections made in steps prior tocalculating those outputs. At the same time, fidelity in modeling may beimportant. Acquiring an accurate model may ensure accuracy in quantitiesof interest computed based on the model.

In some instances, quantities of interest may be especially affected bygeometry in a model. In other words, uncertainty of models, for example,due to motion and registration artifacts, blooming artifacts, etc. mayimpact computation of quantities of interest. Geometry sensitivity,then, may quantify the amount to which uncertainty in geometry impactscomputing of quantities of interest.

Thus, in the context of guided workflows, a need exists for focusingattention on regions of a model that exhibit higher sensitivity. Theseregions may be specific regions of an image where calculations ofquantities of interest may be sensitive or susceptible to geometryand/or uncertainty in geometry. A need exists for identifying regions ofgeometric models based on sensitivity, and creating workflows thatpermit attention to and/or correction of these regions.

The present disclosure is directed to a new approach of guiding ordesigning workflows. More specifically, the approach describes usinggeometry sensitivity information to guide workflow. The method may beapplied to guide workflow where geometry may be important. In oneembodiment, geometry may be estimated (e.g., from an image or scanner)or input directly. Such geometry may contain some degree of uncertainty.A quantity of interest may change in response to an input variable,where an exemplary input variable is related to geometric dimensions(e.g., uncertainty in diameter). The degree of this change and/or therate of the change may be defined as, sensitivity. In other words,sensitivity may be defined as a rate of change in a quantity of interestrelative to a unit change in an input variable.

The exemplary approach described includes guiding a workflow processbased on geometry sensitivity, such as the degree to which uncertaintyin geometry influences determinations of quantities of interest. Forexample, sensitivity information may be used to focus attention onparticular regions of an image that may be sensitive to reconstructedgeometry. Overall, the present disclosure is directed to a type ofworkflow process that may include one or more of the following steps:(i) receiving an input, such as raw, unprocessed data (e.g., imagingdata), (ii) constructing a geometrical model using the input, (iii)filtering and processing the geometrical model to create one or moreregions of interest, and (iv) performing computational analysis tocalculate quantities of interest associated with one or more regions ofinterest. In one embodiment, the disclosure may focus on a step between(iii) and (iv), where some aspect of the model is computed to affect theworkflow (e.g., guide user interaction with the model) beforecomputational analysis is performed. Sensitivity to geometry may be anexemplary aspect of the model computed.

In some cases, geometry sensitivity may be defined as the standarddeviation in a quantity of interest, due to uncertainty in the geometry.In some embodiments, geometry sensitivity information may help quantifythe importance of a local geometry on one or more quantity of interestcalculations. For example, sensitivity to geometry may be a usefulmetric in various applications, including quantifying uncertainty in airflow patterns and drag across the wing of an aircraft, optimizing shapesof automobiles to minimize draft and lift coefficients, computer-aideddesign (e.g., design of space vehicles, construction of buildings,design of bridges, design of prosthetics), reconstruction of organs andtransport arteries from medical imaging data, etc. For medical imagingdata, uncertainty in geometry may arise due to motion and registrationartifacts, blooming artifacts, etc. In general, relationships betweengeometry and quantities of interest may be complex. For example, suchrelationships may be described by ordinary or partial differentialequations. In some cases, calculating the impact of geometry on aquantity of interest may involve solving stochastic differentialequations, which are computationally intensive and challenging to solve.In other cases, governing equations may be less complex. Even so,calculating sensitivity to geometry may be burdensome, involving stepsincluding (i) parameterizing the geometry, (ii) reducing the continuousfinite dimensional space of geometry to a finite dimensional subspace,and (iii) implementing efficient stochastic algorithms to quantifysensitivity.

The present disclosure is directed to facilitating the creation ofaccurate models, such as models in preparation for computationalanalysis. Specifically, the present disclosure may include formingaccurate models by way of using geometry sensitivity information to makeguided workflows directed to model creation. The present disclosure mayinclude several methods for designing or directing workflows to focusattention on regions identified as having higher sensitivity andpossibly requiring attention or correction. The method of the disclosuremay be applied directly on parameterized or constrained geometries, forexample, where geometric surfaces are constrained to be non-uniformrational B-spline (NURBS) surfaces.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for using geometry sensitivity informationto guide workflow. Specifically, FIG. 1 depicts a plurality ofphysicians 102 and third party providers 104, any of whom may beconnected to an electronic network 100, such as the Internet, throughone or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third party providers 104 may create or otherwiseobtain images of one or more patients' cardiac and/or vascular systems.The physicians 102 and/or third party providers 104 may also obtain anycombination of patient-specific information, such as age, medicalhistory, blood pressure, blood viscosity, etc. Physicians 102 and/orthird party providers 104 may transmit the cardiac/vascular imagesand/or patient-specific information to server systems 106 over theelectronic network 100. Server systems 106 may include storage devicesfor storing images and data received from physicians 102 and/or thirdparty providers 104. Server systems 106 may also include processingdevices for processing images and data stored in the storage devices.

FIG. 2 is a block diagram of an exemplary method 200 for using geometrysensitivity information for guiding workflow, according to an exemplaryembodiment of the present disclosure. Method 200 may be performed byserver systems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network100. The method of FIG. 2 may include receiving information about atarget object's geometry, as well as a quantity of interest related tothe object geometry (step 201). For example, the input geometry of atarget object may be specified analytically (e.g., Bezier splines in theform of implicit functions (e.g., level set functions)). Input geometrymay also include or be derived from any variety of images, including rawimages acquired from a scan from computed tomography, magnetic resonanceimages, ultrasound images, images from a 3-D scanner, etc. The inputdata may be used to create a digital representation of the geometry ofthe target object, including regions of interest. In one embodiment, thedigital, geometric model representation of the target object may beextracted from the input information. For instance, geometry may beisolated and extracted from input images. The geometric model may beconstructed using image intensity and gradient measures of the rawimage, using prior knowledge and statistical methods, such as Bayesian,maximum likelihood estimates, manifold learning, and/or machinelearning. In one instance, the quantity of interest may relate togeometries that may vary in time and space.

In one embodiment, the geometric model may then be subdivided so thateach region may be mapped to a sensitivity value (step 203). Either eachregion may be considered a single independent random variable, orvarious geometric regions may have sensitivity values that arecorrelated to each other. In one embodiment, subdivisions may be equallyspaced components, produced by splitting the geometry evenly.Alternately, geometric regions may be based on salient locations of thegeometry.

In one embodiment, the next step may include determining someuncertainty measure associated with each subdivision (step 205). Theuncertainty may be related to imaging modality acquisition protocol,reconstruction method, etc. For example, the measure may include theform and magnitude of uncertainty. Form of uncertainty may be based onprobability distribution functions, the most common being Gaussian andUniform distributions. Magnitude of uncertainty may include a magnitudeof an uncertainty associated with an input used to calculate a quantityof interest. Using the embodiment previously described, magnitude mayentail uncertainty in geometry, such as, specifically, geometry that maybe used to calculate a quantity of interest. An appropriate magnitude ofuncertainty may be assigned for each associated, selected sub-region.Sensitivity may then be an uncertainty in an output quantity ofinterest, calculated based on geometry input into the calculation of thequantity of interest.

In one embodiment, the next step may include determining the sensitivityvalue of the quantity of interest for each subdivision (step 207). Inone embodiment, step 207 may include determining a functionalrelationship between a geometry and a quantity of interest. Step 207 mayfurther include calculating the quantities of interest for a finitegeometry (e.g., subdivision), then generating a histogram of thequantities of interest for that geometry. Step 207 may determine that astandard deviation calculated from the histogram is the sensitivityvalue assigned to each subdivision of the geometry, for a particularquantity of interest. Step 207 is described in further detail in FIG.3A.

In one embodiment, the step afterwards may include providing userguidance (step 209) based on the sensitivity information. For example, auser may be guided to inspect subdivisions that have sensitivities abovea certain threshold. In one embodiment, the user may be guided via apresentation in which the subdivisions with the greatest sensitivity arehighlighted on a representation of the target object. Alternately or inaddition, the internal workflow of the system may adjust to guide a userto inspect the most sensitive subdivisions. The size or resolution ofsubdivisions displayed for inspection may be dynamic. An exemplarymethod for determining resolution-based sizes of subdivision isdisclosed, for example, in U.S. Provisional Application No. 61/948,325,filed Mar. 5, 2014, entitled “Method and System for GeometricSensitivity Prediction Using Machine Learning,” which is herebyincorporated by reference herein in its entirety.

In one embodiment, step 209 may include calculating the sensitivity(σ_(q)) for each geometric segment, then calculating the maximum σ_(q)to determine a geometric segment where geometry may add the greatestuncertainty to a quantity of interest. Step 209 is described in furtherdetail in FIG. 4.

FIG. 3A is a block diagram of an exemplary method 300 for step 207 ofdetermining the sensitivity of the quantity of interest for eachsubdivision, according to an exemplary embodiment of the presentdisclosure. Method 300 may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 100. Determining thesensitivity may serve to quantify the impact of the uncertainty set outin step 205 of method 200. Quantifying the impact of the uncertainty mayhelp assess the functional significance of uncertainty in each geometricsub-region for a quantity of interest. To determine the sensitivity foreach subdivision, method 300 may first include calculating, retrieving,or obtaining a quantity of interest (q) associated with each modeland/or each geometry (step 301). For example, users may be interested indifferent quantities of interest, depending on a type of modelevaluated. For instance, a quantity of interest of a model including acoronary vessel may include a coronary resistance, a flow, a pressure, afractional flow reserve (FFR), etc. Thus, step 301 may includecalculating values of the quantity of interest at one or more locationsof the model and/or geometry, and the calculated values of the quantityof interest at any location of the model and/or geometry may bedependent on changes in or sensitivity of geometry at any of one or moreof the identified subdivisions. Step 303 may include aggregating theq_(i). For example, aggregation may be performed by sampling astochastic space using an assigned probability distribution for eachgeometric parameter. Such aggregation may further include generating ahistogram of the samples. Step 305 may include calculating a standarddeviation based on the histogram from step 303, where the sensitivity(σ_(q)) may be the standard deviation assigned to each subdivision ofthe geometric item of interest. In one embodiment, step 300 may furtherinclude generating a map wherein sensitivity is mapped to eachsub-region (step 307).

FIG. 3B is a block diagram of an exemplary method 320 for calculatingthe q_(i), according to an exemplary embodiment of the presentdisclosure. Method 320 may also be performed by server systems 106,based on information, images, and data received from physicians 102and/or third party providers 104 over electronic network 100. First,method 320 may include determining a functional relationship betweengeometry (G) and a quantity of interest (q) (step 321). In oneembodiment, geometry (G) may be geometry from the geometric model fromsteps 201 or 203. Furthermore, the functional relationship betweengeometry (G) and quantity of interest (q) may be a complex functionalrelationship, not trivial to obtain. In other words, step 321 mayinclude finding a functional relationship, q=f(G). Next, quadraturepoints associated with a specific geometry (G) may be found (step 323).In one embodiment, the quadrature points may be found using a stochasticcollocation algorithm, which may calculate q's at the quadrature pointsusing, for example, the Smolyak sparse grid algorithm, where eachquadrature point corresponds to a specific geometry. In one embodiment,step 325 may include determining or identifying a set of geometries(G_(i)) For example, G_(i) may include a set of geometries for whichq_(i)=f(G_(i)) applies. In any case, step 325 may include determining aset of geometries (G_(i)) for which to calculate associated q_(i).Consequently, step 327 may include calculating q_(i) corresponding toG_(i), for example, based on q_(i)=f(G_(i)).

FIG. 4 is a block diagram of an exemplary method 400 for step 209 ofguiding users to inspect subdivisions based on sensitivity, according toan exemplary embodiment of the present disclosure. Method 400 may beperformed by server systems 106, based on information, images, and datareceived from physicians 102 and/or third party providers 104 overelectronic network 100. In one embodiment, step 401 may includereceiving sensitivity values for respective subdivisions, for example,as calculated by method 300. Step 403 may include determining a cutoff,or threshold, sensitivity value. Such a threshold may provide the basisfor rendering a presentation of the model for inspection. In oneembodiment, step 403 may include identifying one global cutoff for ageometric model, where sensitivity values may dictate whether a regionor subdivision is highly sensitive. For example, sensitivity valuesabove one threshold may result in a geometric region of one color, whilesensitivity values above another threshold may correspond to a geometricregion of another color.

In another, further embodiment, step 403 may include identifyingmultiple thresholds, each of varying sensitivity. For example, step 403may include determining threshold values specific to subdivisions. Forinstance, threshold values may vary across various regions orsubdivisions of a geometric model. In an exemplary case where ageometric model includes a model of coronary arteries, a threshold valuefor proximal regions may differ from a threshold value for distalregions. In addition, threshold values for secondary and/or tertiaryvessels may be different from threshold values for main coronaryarteries.

Step 405 may include determining all locations where sensitivity isabove a threshold, for example, as determined in step 403. Furthermore,step 407 may determine a user interaction that draws attention to thelocations noted in step 405. For example, one such user interaction mayinclude highlighting or color-coding regions on a user-visiblerepresentation, where the colored regions are associated with respectivesensitivity values. A user may know to inspect the highlighted and/orcolor-coded regions and ensure fidelity of geometry. Another example mayinclude alternating a workflow of the system or sequence of steps toguide a user to inspect and/or prioritize more sensitive subdivisions.For instance, highly sensitive regions may be highlighted in a givencolor (e.g., red), throughout a workflow, as a user is inspecting theregions so that a user may track the region from view to view in aworkflow. In one embodiment, the workflow sequence and/or coloring ofthe regions may change as a user inspects and/or interacts with regions.As a further example, step 407 may include identifying whether manualinspection is desired and generating user interaction channels thatpromote and guide the manual inspection. For example, step 407 mayinclude determining conditions that may help identify a situation wheremanual inspection is desired. Then, step 407 may include prompts thatrequire users to approve or alter an image prior to continuing in aworkflow toward a completed, approved geometric model. Step 409 mayfurther include outputting a sensitivity map based on the calculatedsensitivity values' associations with locations in the geometric model.In one embodiment, the sensitivity map output by step 409 may includethe user-visible representations for user inspection. In anotherembodiment, a sensitivity map may also include a final sensitivity map,after user inspection and approval.

FIGS. 5A-5C are block diagrams of a specific embodiment of method 200 ofusing geometry sensitivity information for guiding workflow, accordingto an exemplary embodiment. Methods 500-540 may be performed by serversystems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network100. The specific embodiment, method 500, describes application ofmethod 200 to a technique of calculating sensitivity of fractional flowreserve to geometry information for performing patient-specificestimation. Such a technique may be especially useful for patients withheart disease. In one embodiment, step 501 may include acquiring adigital representation of various patient information. In the case of apatient suspected of having coronary disease, step 501 may includedetermining desired patient information and obtaining the medicalimages, clinical information, and/or patient-specific measurementsdesired for subsequent steps. Various embodiments of such a method andsystem of computing a geometric model to noninvasively determineinformation relating to blood flow is described in greater detail inU.S. Pat. No. 8,315,812, filed Jan. 25, 2011, and entitled “Method andSystem for Patient-Specific Modeling of Blood Flow,” which is herebyincorporated by reference in its entirety. Further detail of anembodiment of step 501 is provided in FIG. 5B.

After collecting desired information, step 503 may includereconstructing coronary tree geometry for a geometric model of a targetobject. Step 503 may be achieved through any reconstruction method,including ostia detection, centerline path reconstruction,reconstruction of vessel lumen, trimming vessels, etc. Ostia detectionmay include identifying, for a patient, locations where coronaryarteries originate from the aorta, meaning one or more ostium. Vesselsnear ostia may be critical for transporting oxygenating blood from theaorta to the entire coronary tree, so identifying each ostium and thelocation of each ostium may be critical in performing coronary relatedsimulations. A machine learning method, along with an understanding ofthe unique bifurcation pattern at the ostia, may be used toautomatically detect location of ostia.

Centerline path reconstruction may include generally identifying thestructure of coronary arteries. Centerlines may be fictional path linesthat pass inside coronary arteries. They may be useful in estimating thenumber and location of bifurcations, and when taken collectively,centerlines may help form reconstructions of coronary artery geometry.Centerline paths may be reconstructed automatically orsemi-automatically, and centerline paths may be reconstructed usingconnectivity of a contrast agent in a vessel lumen, by fitting models ofshape and appearance to image data, using region growing techniques andconnected component analysis, by employing optimization using vesselnessmeasures, etc.

Reconstruction of a vessel lumen may include incorporating the locationof centerlines, along with raw image(s) of pixel intensities and machinelearning algorithm(s) trained on a database of images with a groundtruth lumen. The reconstruction of a vessel lumen may includeassociating probabilities for a finite set of candidate lumens, where areconstruction of a vessel lumen is based on a maximum likelihoodestimate that a candidate lumen, in fact, portrays a lumen of interest.Reconstructions involving trimming vessels may be taken into accountsince a location of trimming may affect a resultant coronary treegeometry of interest, as well as associated quantities of interest andsensitivity measures. Trimming, or truncating, geometry and modeling themicro-vessels and capillary arteries using lumped parameters that dependon trim plane location, means that trimming may impact a geometric modeland quantity of interest computed from the model. In the presentembodiment where a quantity of interest is focused on what is travelingthrough the micro-vessels and arteries, it may be preferable for ageometric model (and associated quantity of interest) to not be highlysensitive to trim location. In this context, a fractional flow reserve(FFR) is the quantity of interest. Since FFR concerns geometry inside ofa lumen, less precise trimming may be computed even if trimming is notas precise.

In one embodiment, step 505 may include computing geometry sensitivity.In one embodiment, step 505 may include identifying or defininguncertainty, such as, uncertainty arising from imaging artifacts orreconstruction algorithms, for instance. Next, step 505 may includedetermining associations between geometries that may allow forsensitivity information to apply across a region of a geometry or beisolated to a particular segment in geometry. Step 505 may furtherinclude constructing a stochastic collocation grid for a geometricobject, and then calculating a quantity of interest for each stochasticcollocation point within the stochastic collocation grid. Once thequantity of interest is calculated, the standard deviation andconfidence intervals of the quantity of interest may be found. Thatcalculated standard deviation may constitute the sensitivity. Furtherdetail of step 505 is provided in FIG. 5C.

Lastly, step 507 may include using the sensitivity for a guidedworkflow. Step 507 is an exemplary application of step 209 andcorresponding exemplary method 400. In one embodiment for step 507,sensitivity information may guide geometry construction. For example,step 507 may include assigning a threshold (e.g., a value of 0.05) aftersensitivity information is calculated. In one embodiment, step 507 mayinclude highlighting regions within a coronary tree that havesensitivity values higher than the threshold. A representation includinghighlighted and non-highlighted regions may be presented to a reviewer.In one instance, a reviewer may be directed to inspect highlightedregions to ensure fidelity, for example, of both a reconstructed modeland location of trimming planes as given from reconstruction(s) of step502. As previously discussed, step 507 may also include determiningmultiple thresholds or ranges of thresholds so that there may be variouscolored regions, each depicting some range or level of sensitivity.

Step 507 may further include prompting a manual correction step toreinstate a lumen to a desired size and shape. The new geometry mayaffect sensitivity values, so this aspect of step 507 may triggerrepeating of step 505 to recalculate sensitivity. Alternatively or inaddition to highlighting, step 507 may include guiding a reviewerthrough a series of views in a software program to inspect areas of highsensitivity. In a further embodiment, sensitivities may be saved to anelectronic storage medium to guide future workflows or to resume aninterrupted workflow. The steps of determining geometry and ensuringfidelity of geometry may serve as a precursor to calculating quantitiesof interest.

FIG. 5B is a block diagram of an exemplary method 520 for acquiring adigital representation of various patient information, according to anexemplary embodiment. Method 520 may be a process of acquiring patientdata needed to form the geometry associated with a quantity of interest.Accurately forming the geometry may allow the overall method 500 toaccurately determine sensitivity, thus quantifying the degree to whichvariability in a quantity of interest measurement is attributable togeometry. In one embodiment, step 521 may include obtaining a cardiaccomputed tomography angiography (CCTA) image. However, step 521 mayinclude acquiring any medical images of a patient. In one embodiment,step 523 may include computing a geometric model based on the CCTA orother medical images from step 521. For instance, step 523 may includecomputing a geometric model of all the vessels of interest, includingascending aorta, left/right coronary artery, left circumflex artery,left obtuse marginal, and any other visible vessels of interest. In anexemplary embodiment, a method and system determines informationrelating to blood flow in a specific patient using information retrievedfrom the patient noninvasively.

Step 525 may include obtaining clinical parameters. For example, a setof clinical parameters obtained in step 525 may include measurements forheart rate, systolic and diastolic brachial blood pressures, hematocrit,patient height and weight, and patient history (e.g., smoking status,presence/absence of diabetes, etc.).

Step 527 may include calculating quantities based on step 521 and step525. The derived quantities may include myocardial mass, body surfacearea, viscosity, inlet aortic flow rate, coronary flow rate, coronaryresistance, and resistance of outlet aorta. Myocardial mass (m_(myo))may be obtained using image segmentation of the left ventricle. Forexample, the segmentation may help calculate the volume of myocardium,which may be multiplied with a density (usually assumed to be constantat ˜1.05 g/cm³). Body surface area may be calculated from patient height(h) and weight

${BSA} = {\sqrt{\frac{hw}{3600}}.}$

Viscosity may be calculated from hematocrit (hem) as

${\left. {\eta = \frac{c}{\left( {1 - \frac{hem}{100}} \right.}} \right)^{2.5} \div},$

where c may be taken as 0.0012. Inlet aortic flow rate (Q) may becalculated from scaling studies as

$Q = {\frac{1}{60}{{BSA}^{1.15}.}}$

In one example, coronary flow rate (q_(cor)) may be calculated frommyocardial mass as

$q_{cor} = {c_{dil}\frac{5.09}{60}m_{myo}^{0.75}}$

where c_(dil) may denote the dilation factor. Coronary resistance mayinclude calculating a net coronary resistance from the desired coronaryflow. Resistance value for individual outlets may be calculated based onareas of the respective outlets. Resistance of the outlet aorta may becalculated based on aortic pressure, aortic flow rate, and desiredcoronary flow rate. Obtaining all the values from steps 521-527 mayprovide the basis for reconstructing coronary tree geometry (step 503).

FIG. 5C is a block diagram of an exemplary method 540 for computinggeometry sensitivity, according to an exemplary embodiment. Method 540may be one possible embodiment of performing step 505 for determininggeometry sensitivity. In one embodiment, step 541 may include defininguncertainty in geometry. For example, various reconstructions orgeometries from step 502 (or related steps 201 and 203 from generalmethod 200) may be associated with different types of uncertainties. Forexample, a reconstruction of a vessel lumen (as described for step 503of method 500) may be considered a statistical realization over apossible range of geometries. In other words, the model of vessel lumengeometry from step 503 may be an estimate based on various geometries.The reconstruction may be an approximation, over a possible range ofgeometries, of a patient's actual vessel lumen. Such an uncertainty mayarise from image noise, artifacts, or the reconstruction algorithm usedfor step 503. In one embodiment, step 541 may include determining aprobability distribution assigned to a family of geometries, withinwhich a “true geometry” may lie. Such a distribution may be data-driven,or a Gaussian or Uniform distribution.

Step 543 may include determining one or more correlations betweensubdivisions or segments of geometry. For example, step 543 may includesplitting patient-specific geometry (from step 503) into regions.Continuing from the example of a reconstruction of a vessel lumen, step543 may include splitting the patient-specific geometry (e.g., thereconstruction) into regions based on bifurcation locations. Any segmentbetween two bifurcations, ostium and bifurcation, and/or ostium andtrimmed outlet node may be mapped as an independent random variable.This may mean uncertainty in geometry within a segment may be fullycorrelated, and uncertainty across segments may be uncorrelated. In oneinstance, step 543 may further include determining subdivisions ofgeometry based on sensitivity. For example, if sensitivity of a segmentis deemed higher than a threshold value, step 543 may include dividingthe segment further into two equal segments, which are designated to beuncorrelated (e.g., independent random variables). From there, step 543may prompt resuming the sensitivity analysis. In one case, sensitivityanalysis may be terminated when either (i) there are no segments whosesensitivities are above a threshold value, or (ii) segments cannot besplit anymore (e.g., as governed by resolution of centerline points. Inone embodiment, the situation (i) of no segments being associated withsensitivities above a threshold value may cause, for instance, a promptto retrieve more input images and/or resetting of output values.

Step 545 may include constructing a stochastic collocation grid in orderto calculate a quantity of interest with respect to geometries withinthe family of geometries and estimate solutions for geometries outsideof the family of geometries. For example, interpolation may be used toestimate solutions. For each independent segment, the set of possiblegeometries may be infinite (e.g., due to continuous probabilitydistributions). First, then, step 545 may include mapping the infiniteset of possible geometries to a finite number. The finite set ofpossible geometries may comprise a probability set, where solutionscorresponding to any other geometry in the probability set (e.g.,solutions outside the finite set or outside the family of geometries)may be obtained using interpolation in a stochastic space. In one case,a Smolyak sparse grid algorithm may be used to identify a set ofcollocation points, where each point may correspond to a uniquegeometry. The algorithm may be repeated for each geometric segmentidentified, for example, in step 543.

Step 547 may include calculating sensitivities. For example, thequantity of interest for method 500 may be Fractional Flow Reserve(FFR), which may be the ratio of local to aortic pressure or local toaortic flow. In one embodiment, FFR may be computed at each stochasticcollocation point. Once FFR is calculated for various uncertainties,step 547 may include constructing a stochastic space representation ofFFR. For example, step 547 may include calculating FFR for all sourcesof uncertainties identified in step 541, at each collocation point. Thestochastic space representation of FFR may then be a representationbased on all the uncertainties determined from step 541. In otherinstances, the representation may be based on a subset of theuncertainties. In one embodiment, the stochastic space may be sampled tocalculate standard deviation and confidence intervals of FFR.Sensitivity may be defined as the standard deviation of FFR. Calculatingthe standard deviation of FFR may thus mean calculating the sensitivity.In one case, a machine learning algorithm may be used to calculate FFRfor the numerous geometric segments and stochastic collocation points.In another case, blood flow simulations may be used to calculate FFR.The blood flow simulations for calculating FFR may be more suitable forcases with fewer geometric segments or stochastic collocation points.Various embodiments of such a method and system for determininguncertainty related to quantities of interest are described in greaterdetail in U.S. Nonprovisional application Ser. No. 13/864,996 entitled“Method and System for Sensitivity Analysis in Modeling Blood FlowCharacteristics,” filed Apr. 17, 2013, the entire disclosure of which ishereby incorporated by reference in its entirety.

FIG. 6 is a block diagram of an exemplary method 600 of a workflowprocess guided using sensitivity information, according to an exemplaryembodiment. In one embodiment, step 601 may include obtaining images fora geometric model, for example, computed tomography (CT) scans. Thescans may undergo image pre-processing (step 603), where the processedimages may then be used for a 3-D model reconstruction (step 605). Thismodel may be evaluated through a guided review, where accuracy of themodel may be verified (step 607). Lastly, a verified model from step 607may serve as a computational model and/or basis for simulations (step609). The computations and simulations may produce quantities ofinterest (step 611). In one embodiment, pre-processing may includedetermination of uncertainty in lumen boundaries and/or determination ofconfidence in lumen extraction, which may then require inlet or outlettrimming. Based on the pre-processing, sensitivity may be determined(step 613), where sensitivity may specifically be geometry sensitivity.This sensitivity information may inform guided review (step 607),computations and simulations (step 609), and determinations ofquantities of interest (step 611). In addition, images for a geometricmodel may undergo centerline extraction (step 615), lumen extraction(step 617), and/or inlet/outlet trimming (step 619) in preparation forcreating the 3-D model of step 605. In one embodiment, guided review(step 607) may repeat steps 617 and 619 of lumen extraction andinlet/outlet trimming based on user input and/or newly receivedinformation.

FIG. 7 is a diagram of an exemplary user interface 700 that a user maysee as part of a guided workflow, according to an exemplary embodiment.In one embodiment, darker regions 701 may denote regions wheresensitivity values exceed a threshold value for sensitivity. Lighterregions 703 may show geometric regions where sensitivity values fallbelow the threshold. In this instance, a user may be prompted to examineeach of the darker regions 701 more carefully. Alternately oradditionally, a user may be informed of a sensitivity value, such aswhen the user moves, touches, points to, clicks on, or hovers a mouseover parts of the user interface 700. A user may take this sensitivityvalue into account to decide whether to rely on computed quantities ofinterest.

In summary, workflows may be guided based on various criteria. Insimulations and computations using geometric models, quantities ofinterest may be susceptible to uncertainty, the degree of which isattributable to geometry (i.e., “geometry sensitivity,” may assist inunderstanding how much a calculation for a quantity of interest may beaffected by geometry). Thus, the presently disclosed method enablesdetermining geometry sensitivity for the purpose of using the geometrysensitivity information to guide workflows.

FIG. 8 is a simplified block diagram of an exemplary computer system 800in which embodiments of the present disclosure may be implemented, forexample as any of the physician devices or servers 102, third partydevices or servers 104, and server systems 106. A platform for a server800, for example, may include a data communication interface for packetdata communication 860. The platform may also include a centralprocessing unit (CPU) 820, in the form of one or more processors, forexecuting program instructions. The platform typically includes aninternal communication bus 810, program storage and data storage forvarious data files to be processed and/or communicated by the platformsuch as ROM 830 and RAM 840, although the server 800 often receivesprogramming and data via a communications network (not shown). Thehardware elements, operating systems and programming languages of suchequipment are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. The server 800also may include input and output ports 850 to connect with input andoutput devices such as keyboards, mice, touchscreens, monitors,displays, etc. Of course, the various server functions may beimplemented in a distributed fashion on a number of similar platforms,to distribute the processing load. Alternatively, the servers may beimplemented by appropriate programming of one computer hardwareplatform.

As described above, the computer system 800 may include any type orcombination of computing systems, such as handheld devices, personalcomputers, servers, clustered computing machines, and/or cloud computingsystems. In one embodiment, the computer system 800 may be an assemblyof hardware, including a memory, a central processing unit (“CPU”),and/or optionally a user interface. The memory may include any type ofRAM or ROM embodied in a physical storage medium, such as magneticstorage including floppy disk, hard disk, or magnetic tape;semiconductor storage such as solid state disk (SSD) or flash memory;optical disc storage; or magneto-optical disc storage. The CPU mayinclude one or more processors for processing data according toinstructions stored in the memory. The functions of the processor may beprovided by a single dedicated processor or by a plurality ofprocessors. Moreover, the processor may include, without limitation,digital signal processor (DSP) hardware, or any other hardware capableof executing software. The user interface may include any type orcombination of input/output devices, such as a display monitor,touchpad, touchscreen, microphone, camera, keyboard, and/or mouse.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms, such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1. A computer-implemented method of guiding a workflow for refining a geometric model, using a computer system, the method comprising: receiving (1) a geometric model of a target object and geometric parameters that define the geometry of the geometric model; (2) a type of a quantity of interest that can be calculated for various points in the geometric model; and (3) a threshold value of a sensitivity of the quantity of interest to geometry of the geometric model; calculating one or more values of the quantity of interest at a plurality of points in the geometric model, by iterating one or more values of the geometric parameters; calculating, for each of the plurality of points in the geometric model, a sensitivity value characterizing a variability measure of an extent to which changes in the calculated one or more values of the quantity of interest are attributable to changes in the iterated one or more values of the geometric parameters; comparing the sensitivity value calculated at each of the one or more plurality of points to the threshold value of the sensitivity; and generating or altering, using a processor, a display of one or more points where the sensitivity value exceeds the threshold value of the sensitivity.
 2. The method of claim 1, further including: determining the threshold value of the sensitivity; and determining one or more workflow sequences including the display of the one or more points where the sensitivity value exceeds the threshold value of the sensitivity.
 3. The method of claim 2, wherein the one or more workflow sequences include one or more representations, user prompts, workflow steps, or a combination thereof, wherein the one or more representations include highlighting one or more portions of the geometric model based on the sensitivity value, the threshold value of the sensitivity, or a combination thereof.
 4. The method of claim 2, further including: determining one or more subdivisions in the geometric model; and determining the one or more workflow sequences based on the one or more subdivisions, wherein the sensitivity value is associated with the one or more subdivisions.
 5. The method of claim 4, further including: determining a subset of the one or more subdivisions for which the sensitivity value exceeds the threshold value of the sensitivity; and determining one or more visible representations particular to the subset of the one or more subdivisions.
 6. The method of claim 2, further including: determining one or more conditions for manual inspection associated with the geometric model; and determining the one or more workflow sequences based on the one or more conditions for manual inspection.
 7. The method of claim 2, further including: determining one or more user corrections associated with the geometric model; and determining the one or more workflow sequences based on the one or more user corrections.
 8. The method of claim 1, wherein the variability measure includes a standard deviation of the quantity of interest associated with the geometric parameters.
 9. A system of guiding a workflow for refining a geometric model, the system comprising: a data storage device storing instructions for guiding a workflow using geometry sensitivity information; and a processor configured to execute the instructions to perform a method including: receiving (1) a geometric model of a target object and geometric parameters that define the geometry of the geometric model; (2) a type of a quantity of interest that can be calculated for various points in the geometric model; and (3) a threshold value of a sensitivity of the quantity of interest to geometry of the geometric model; calculating one or more values of the quantity of interest at a plurality of points in the geometric model, by iterating one or more values of the geometric parameters; calculating, for each of the plurality of points in the geometric model, a sensitivity value characterizing a variability measure of an extent to which changes in the calculated one or more values of the quantity of interest are attributable to changes in the iterated one or more values of the geometric parameters; comparing the sensitivity value calculated at each of the one or more plurality of points to the threshold value of the sensitivity; and generating or altering, using a processor, a display of one or more points where the sensitivity value exceeds the threshold value of the sensitivity.
 10. The system of claim 9, wherein the processor is further configured for: determining the threshold value of the sensitivity; and determining one or more workflow sequences including the display of the one or more points where the sensitivity value exceeds the threshold value of the sensitivity.
 11. The system of claim 10, wherein the one or more workflow sequences include one or more representations, user prompts, workflow steps, or a combination thereof, wherein the one or more representations include highlighting one or more portions of the geometric model based on the sensitivity value, the threshold value of the sensitivity, or a combination thereof.
 12. The system of claim 10, wherein the processor is further configured for: determining one or more subdivisions in the geometric model; and determining the one or more workflow sequences based on the one or more subdivisions, wherein the sensitivity value is associated with the one or more subdivisions.
 13. The system of claim 12, wherein the processor is further configured for: determining a subset of the one or more subdivisions for which the sensitivity value exceeds the threshold value of the sensitivity; and determining one or more visible representations particular to the subset of the one or more subdivisions.
 14. The system of claim 9, wherein the processor is further configured for: determining one or more conditions for manual inspection associated with the geometric model; and determining the one or more workflow sequences based on the one or more conditions for manual inspection.
 15. The system of claim 9, wherein the processor is further configured for: determining one or more user corrections associated with the geometric model; and determining the one or more workflow sequences based on the one or more user corrections.
 16. The system of claim 9, wherein the variability measure includes a standard deviation of the quantity of interest associated with the geometric parameters.
 17. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for guiding a workflow for refining a geometric model using the method comprising: receiving (1) a geometric model of a target object and geometric parameters that define the geometry of the geometric model; (2) a type of a quantity of interest that can be calculated for various points in the geometric model; and (3) a threshold value of a sensitivity of the quantity of interest to geometry of the geometric model; calculating one or more values of the quantity of interest at a plurality of points in the geometric model, by iterating one or more values of the geometric parameters; calculating, for each of the plurality of points in the geometric model, a sensitivity value characterizing a variability measure of an extent to which changes in the calculated one or more values of the quantity of interest are attributable to changes in the iterated one or more values of the geometric parameters; comparing the sensitivity value calculated at each of the one or more plurality of points to the threshold value of the sensitivity; and generating or altering, using a processor, a display of one or more points where the sensitivity value exceeds the threshold value of the sensitivity.
 18. The non-transitory computer readable medium of claim 17, the method further comprising: determining the threshold value of the sensitivity; and determining one or more workflow sequences including the display of the one or more points where the sensitivity value exceeds the threshold value of the sensitivity.
 19. The non-transitory computer readable medium of claim 18, wherein the one or more workflow sequences include one or more representations, user prompts, workflow steps, or a combination thereof, wherein the one or more representations include highlighting one or more portions of the geometric model based on the sensitivity value, the threshold value of the sensitivity, or a combination thereof.
 20. The non-transitory computer readable medium of claim 18, the method further comprising: determining one or more subdivisions in the geometric model; and determining the one or more workflow sequences based on the one or more subdivisions, wherein the sensitivity value is associated with the one or more subdivisions. 